Most taxi companies deliver taxies upon requests from customers either by telephones, mobile applications (or apps) or the Internet. They typically face the problem of how to place taxi stands (or stations) and allocate standby taxies while minimizing operational cost and maintaining satisfactory response time. Smart city planners are also interested in knowing how the optimal configuration of such public resources as taxi stands can benefit the overall population.
Optimal allocation of taxi stations and standby taxies can be especially difficult when the region for planning is the entire city. One reason is that it is often hard to collect enough information about real-time taxi demand and traffic conditions within the whole city region. Therefore, the optimization “objective” is hardly defined. Another reason is because optimal allocation of taxi stations consists of choosing an optimal configuration of locations, within the city, from many possible configurations. Such problem has proven to be non-deterministic polynomial-time (NP) hard. Conventional methods often rely on linear programming, which is computationally expensive, particularly when the problem size is as large as a city region.
Therefore, there is a need for an improved framework that addresses the above-mentioned challenges.